Editor’s note: Building on ScienceSoft’s 16 years of experience in software development for healthcare organizations, SaaS companies and medical device manufacturers, Alena describes the possibilities of voice recognition technology in the healthcare sector and shows how to avoid its main implementation risks. If you are interested in the opportunities that voice recognition functionality can bring to your medical software product, you can turn to our medical computer experts.
The accuracy of modern voice recognition technology has reached 99%. Being so technically mature, it is becoming more and more popular among care providers as it produces impressive results. For example, most respondents noted an improvement in the quality and completeness of the documentation, as well as a decrease in the time spent documenting an encounter with a patient after implementing a voice recognition solution.
Before these numbers make you rush to create your own voice recognition solution or add speech recognition to your healthcare product, I’d like to explain the basics of this technology to you and make sure you understand it. the specific features and that you are prepared to deal with possible (but completely manageable) implementation difficulties that may arise.
There are 2 types of voice recognition (SR), which can be used separately or in conjunction with your mobile or web application.
With background voice recognition technology, spoken words are recorded in a digital format and then translated into text. The system sends a draft document generated with a voice record for correction to a medical transcriber or doctor. This type of SR technology is popular among healthcare organizations because the generated texts are edited by one person, which makes them more reliable for elaboration. health documents i EHR records.
Frontal voice recognition software converts spoken words into text in real time. With this type of voice recognition technology, you can eliminate the need for medical transcribers. However, frontal voice recognition technology can provide results with slight errors first, so medical staff should correct transcription errors immediately after entry. Over time, frontal voice recognition software learns speech patterns and avoids even more similar mistakes. Therefore, a front-end SR solution is more appropriate taking personal notes (errors are not as critical here as in EHR records) or brief medical reports with typical formalized sentences (e.g., “normal discoveries,” where the possibility of error is minimal).
SR increases the productivity of medical staff
ScienceSoft’s healthcare customers point out that most of their doctors spend too much time writing notes, reports, and so on. of the patient, which reduces their productivity. With voice recognition technology, medical staff can free up time spent on data entry by writing their documentation by voice and focusing more on patients. And by increasing the productivity of medical staff, SR helps provide more health care to patients, thus accelerating cash flow to healthcare organizations.
SR facilitates the completeness of medical documentation
When a medical history is created with the help of a speech recognition solution, the possibility of omitting some important information during a visit is minimized. This is especially important when you need to document numerous medical services in a single appointment. This, among other advantages, facilitates cooperation with insurance providers.
RS inspires patient involvement
Voice recognition can be a crucial part of voice assistant solutions for patients. Within these solutions, voice recognition technology can increase the level of patient participation in their treatment process outside the care facility.
For example, patients may ask a voice nursing assistant (who can connect to medical devices) to record their symptoms, provide information about their illness, or general health issues. Thus, voice recognition technology makes these activities more comfortable and time efficient. As a result, patients are likely to be enthusiastically involved in their treatment process.
High cost and long implementation time
I suggest using open source SR engines like Google Cloud Speech-to-Text, Azure Speech to Text, Dragon API, and IBM Watson to reduce the time and costs of implementing speech recognition technology. In addition, to get value early and with minimal risk, you should carry out a comprehensive product roadmap and feature prioritization.
Because the information transmitted to EHR is confidential and must be protected, the problem of HIPAA compliance is acute. Based on the experience of ScienceSoft, I recommend that you design your product with SR technology with security in mind by adding security features (for example, a unique encrypted voice profile in the cloud) that help minimize the risk of leakage. data. In addition, I suggest providing continuous monitoring of the system and conducting periodic security tests after product launch to ensure the confidentiality and integrity of health data.
The specificity of medical language, misspelled or missing words, user accents, and a variety of language patterns can lead to errors in documents, reports, and healthcare notes generated with voice recognition technology. To avoid them, you should extend the functionality of the product by:
- Add dictionaries for medical specializations.
- Use reminders to make doctors speak more slowly or more clearly when a speech recognition solution detects a critical amount of disfluency during speech to minimize the possibility of misinterpretations.
- Using semi-auto-correction, which allows users to correct spelling mistakes quickly and easily.
I believe that voice recognition technology offers great opportunities for caregivers and patients. SaaS companies and medical device manufacturers should not ignore this benefit opportunity despite the potential implementation challenges along the way. Therefore, if you plan to enrich your product with voice recognition technology, the ScienceSoft team will be happy to help you.
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